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Record W2895101605 · doi:10.1055/s-0038-1672124

All-Polyethylene Tibial Components in Total Knee Replacement: Early Failures

2018· article· en· W2895101605 on OpenAlexaff
Zaki Suliman Alhifzi, Yousef Tawfik Khoja, Gavin Wood

Bibliographic record

VenueThe Journal of Knee Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineTotal knee replacementKnee replacementSurgeryOrthodonticsPhysical therapyArthroplasty

Abstract

fetched live from OpenAlex

Abstract In total primary knee replacement surgery, the use of all-polyethylene tibial (APT) components has many advantages, including no backside wear and no linear dissociation. In addition, the greater polyethylene thickness permits more conservative bone resection compared with that for metal-backed components, with a lower unit cost and similar functional results. Thus, the use of an APT in primary total knee arthroplasty remains an attractive option. This is a review of 158 patients who underwent primary knee replacement using APT components from a single manufacturer. Data collection included age, American Society of Anesthesiology physical status classification, body mass index (BMI), type of deformity, the presence of diabetes mellitus, rate of revision, and characteristics associated with early failure of the components. Average follow-up time was 40 months. The revision rate for any reason was 5.6%, and the average BMI in revision cases was 37.6. Patients with a higher BMI (≥ 37.6) were significantly more likely to require revision surgery than patients with a lower BMI (p = 0.04). In our sample, high BMI was a contributing factor for early failures in total knee replacements using an APT component. Generally, polyethylene tibial components used for primary knee replacements are safe and effective, with good outcomes and subsequent lower costs to the health care system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.272
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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